Prevalence of self-medication during COVID-19 pandemic: A systematic review and meta-analysis
Bibliographic record
Abstract
Background The COVID-19 pandemic and restrictions on travel and quarantine measures made people turn to self-medication (SM) to control the symptoms of their diseases. Different studies were conducted worldwide on different populations, and their results were different. Therefore, this global systematic review and meta-analysis was conducted to estimate the pooled prevalence of self-medication. Methods In this systematic review and meta-analysis, databases of Scopus, PubMed, Embase, and Web of Science were searched without a time limit. All eligible observational articles that reported self-medication during the COVID-19 pandemic were analyzed. Heterogeneity among the studies was assessed using Cochran's Q test and I 2 statistics. A random-effects model was used to estimate the pooled prevalence of self-medication. The methodological quality of the articles was evaluated with the Newcastle-Ottawa Scale. Results Fifty-six eligible studies were reviewed. The pooled prevalence of self-medication was 48.6% (95% CI: 42.8–54.3). The highest and lowest prevalence of self-medication was in Asia (53%; 95% CI: 45–61) and Europe (40.8%; 95% CI: 35–46.8). Also, the highest and lowest prevalence of self-medication was related to students (54.5; 95% CI: 40.8–68.3) and healthcare workers (32.5%; 16–49). The prevalence of self-medication in the general population (48.8%; 40.6–57) and in patients with COVID-19 (41.7%; 25.5–58). The prevalence of self-medication was higher in studies that collected data in 2021 than in 2020 (51.2 vs. 48%). Publication bias was not significant ( p = 0.320). Conclusion During the COVID-19 pandemic, self-medication was highly prevalent, so nearly half of the people self-medicated. Therefore, it seems necessary to provide public education to control the consequences of self-medication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".